



'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCAmelCase:   List[str]					=		logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase:   List[str]					=		'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'
def  lowerCamelCase__			(		_A					,     _A					,     _A=8							):
  a		:   str	  =	height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  a		:   List[Any]	  =	width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor
def  lowerCamelCase__			(		_A					,     _A=512					,     _A=512							):
  a		:   List[Any]	  =	pil_image.resize((w, h)					,     resample=Image.BICUBIC					,     reducing_gap=1							)
  a		:   Optional[int]	  =	np.array(pil_image.convert('RGB'							)							)
  a		:   List[Any]	  =	arr.astype(np.floataa							) / 127.5 - 1
  a		:   str	  =	np.transpose(_A					,     [2, 0, 1]							)
  a		:   Any	  =	torch.from_numpy(_A							).unsqueeze(0							)
  return image

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Optional[int]		,     __snake_case					:    UNetaDConditionModel		,     __snake_case					:    DDPMScheduler		,     __snake_case					:    VQModel		,     ):
      super().__init__()

      self.register_modules(
          unet=__snake_case		,     scheduler=__snake_case		,     movq=__snake_case		,     )
      a		:   Tuple	  =	2 ** (len(self.movq.config.block_out_channels      ) - 1)







    def      lowercase_			( self					:    List[Any]		,     __snake_case					:    Tuple		,     __snake_case					:    Optional[int]		,     __snake_case					:    Optional[Any]      ):
      # get the original timestep using init_timestep
      a		:   Tuple	  =	min(int(num_inference_steps * strength      )		,     __snake_case      )

      a		:   str	  =	max(num_inference_steps - init_timestep		,     0      )
      a		:   Optional[int]	  =	self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start







    def      lowercase_			( self					:    str		,     __snake_case					:    Union[str, Any]		,     __snake_case					:    Any		,     __snake_case					:    Dict		,     __snake_case					:    List[str]		,     __snake_case					:    List[str]		,     __snake_case					:    Dict		,     __snake_case					:    Optional[int]=None      ):
      if not isinstance(__snake_case		,     (torch.Tensor, PIL.Image.Image, list)      ):
        raise ValueError(
            F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case      )}"""      )

      a		:   Tuple	  =	image.to(device=__snake_case		,     dtype=__snake_case      )

      a		:   int	  =	batch_size * num_images_per_prompt

      if image.shape[1] == 4:
        a		:   Optional[int]	  =	image

      else:
        if isinstance(__snake_case		,     __snake_case      ) and len(__snake_case      ) != batch_size:
          raise ValueError(
              F"""You have passed a list of generators of length {len(__snake_case      )}, but requested an effective batch"""
              F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""      )

        elif isinstance(__snake_case		,     __snake_case      ):
          a		:   Any	  =	[
              self.movq.encode(image[i : i + 1]      ).latent_dist.sample(generator[i]      ) for i in range(__snake_case      )
          ]
          a		:   Tuple	  =	torch.cat(__snake_case		,     dim=0      )
        else:
          a		:   Dict	  =	self.movq.encode(__snake_case      ).latent_dist.sample(__snake_case      )

        a		:   str	  =	self.movq.config.scaling_factor * init_latents

      a		:   Any	  =	torch.cat([init_latents]		,     dim=0      )

      a		:   Optional[int]	  =	init_latents.shape
      a		:   Union[str, Any]	  =	randn_tensor(__snake_case		,     generator=__snake_case		,     device=__snake_case		,     dtype=__snake_case      )

      # get latents
      a		:   Dict	  =	self.scheduler.add_noise(__snake_case		,     __snake_case		,     __snake_case      )

      a		:   List[str]	  =	init_latents

      return latents







    def      lowercase_			( self					:    Dict		,     __snake_case					:    Dict=0      ):
      if is_accelerate_available():
        from accelerate import cpu_offload
      else:
        raise ImportError('Please install accelerate via `pip install accelerate`'      )

      a		:   List[str]	  =	torch.device(F"""cuda:{gpu_id}"""      )

      a		:   int	  =	[
          self.unet,
          self.movq,
      ]
      for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
          cpu_offload(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    Optional[int]=0      ):
      if is_accelerate_available() and is_accelerate_version('>='		,     '0.17.0.dev0'      ):
        from accelerate import cpu_offload_with_hook
      else:
        raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'      )

      a		:   Tuple	  =	torch.device(F"""cuda:{gpu_id}"""      )

      if self.device.type != "cpu":
        self.to('cpu'		,     silence_dtype_warnings=__snake_case      )
        torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

      a		:   Tuple	  =	None
      for cpu_offloaded_model in [self.unet, self.movq]:
        a  ,      a		:   Optional[Any]	  =	cpu_offload_with_hook(__snake_case		,     __snake_case		,     prev_module_hook=__snake_case      )

      # We'll offload the last model manually.
      a		:   Optional[int]	  =	hook







    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def      lowercase_			( self					:    int      ):
      if not hasattr(self.unet		,     '_hf_hook'      ):
        return self.device
      for module in self.unet.modules():
        if (
            hasattr(__snake_case		,     '_hf_hook'      )
            and hasattr(module._hf_hook		,     'execution_device'      )
            and module._hf_hook.execution_device is not None
        ):
          return torch.device(module._hf_hook.execution_device      )
      return self.device







    @torch.no_grad()
    @replace_example_docstring(__snake_case      )
    def __call__( self					:    Optional[int]		,     __snake_case					:    Union[torch.FloatTensor, List[torch.FloatTensor]]		,     __snake_case					:    Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]		,     __snake_case					:    Union[torch.FloatTensor, List[torch.FloatTensor]]		,     __snake_case					:    int = 5_12		,     __snake_case					:    int = 5_12		,     __snake_case					:    int = 1_00		,     __snake_case					:    float = 4.0		,     __snake_case					:    float = 0.3		,     __snake_case					:    int = 1		,     __snake_case					:    Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     __snake_case					:    Optional[str] = "pil"		,     __snake_case					:    bool = True		,     ):
      a		:   Any	  =	self._execution_device

      a		:   Dict	  =	guidance_scale > 1.0

      if isinstance(__snake_case		,     __snake_case      ):
        a		:   Union[str, Any]	  =	torch.cat(__snake_case		,     dim=0      )
      a		:   Tuple	  =	image_embeds.shape[0]
      if isinstance(__snake_case		,     __snake_case      ):
        a		:   Optional[Any]	  =	torch.cat(__snake_case		,     dim=0      )

      if do_classifier_free_guidance:
        a		:   Dict	  =	image_embeds.repeat_interleave(__snake_case		,     dim=0      )
        a		:   Any	  =	negative_image_embeds.repeat_interleave(__snake_case		,     dim=0      )

      a		:   str	  =	torch.cat([negative_image_embeds, image_embeds]		,     dim=0      ).to(dtype=self.unet.dtype		,     device=__snake_case      )

      if not isinstance(__snake_case		,     __snake_case      ):
        a		:   Tuple	  =	[image]
      if not all(isinstance(__snake_case		,     (PIL.Image.Image, torch.Tensor)      ) for i in image      ):
        raise ValueError(
            F"""Input is in incorrect format: {[type(__snake_case      ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""      )

      a		:   Optional[Any]	  =	torch.cat([prepare_image(__snake_case		,     __snake_case		,     __snake_case      ) for i in image]		,     dim=0      )
      a		:   int	  =	image.to(dtype=image_embeds.dtype		,     device=__snake_case      )

      a		:   Union[str, Any]	  =	self.movq.encode(__snake_case      )['latents']
      a		:   Optional[Any]	  =	latents.repeat_interleave(__snake_case		,     dim=0      )
      self.scheduler.set_timesteps(__snake_case		,     device=__snake_case      )
      a  ,      a		:   Union[str, Any]	  =	self.get_timesteps(__snake_case		,     __snake_case		,     __snake_case      )
      a		:   int	  =	timesteps[:1].repeat(batch_size * num_images_per_prompt      )
      a  ,      a		:   Union[str, Any]	  =	downscale_height_and_width(__snake_case		,     __snake_case		,     self.movq_scale_factor      )
      a		:   List[Any]	  =	self.prepare_latents(
          __snake_case		,     __snake_case		,     __snake_case		,     __snake_case		,     image_embeds.dtype		,     __snake_case		,     __snake_case      )
      for i, t in enumerate(self.progress_bar(__snake_case      )      ):
        # expand the latents if we are doing classifier free guidance
        a		:   List[str]	  =	torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

        a		:   Any	  =	{'image_embeds': image_embeds}
        a		:   Any	  =	self.unet(
            sample=__snake_case		,     timestep=__snake_case		,     encoder_hidden_states=__snake_case		,     added_cond_kwargs=__snake_case		,     return_dict=__snake_case		,     )[0]

        if do_classifier_free_guidance:
          a  ,      a		:   Optional[int]	  =	noise_pred.split(latents.shape[1]		,     dim=1      )
          a  ,      a		:   Optional[int]	  =	noise_pred.chunk(2      )
          a  ,      a		:   int	  =	variance_pred.chunk(2      )
          a		:   int	  =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
          a		:   Optional[int]	  =	torch.cat([noise_pred, variance_pred_text]		,     dim=1      )

        if not (
            hasattr(self.scheduler.config		,     'variance_type'      )
            and self.scheduler.config.variance_type in ["learned", "learned_range"]
        ):
          a  ,      a		:   Tuple	  =	noise_pred.split(latents.shape[1]		,     dim=1      )

        # compute the previous noisy sample x_t -> x_t-1
        a		:   Optional[Any]	  =	self.scheduler.step(
            __snake_case		,     __snake_case		,     __snake_case		,     generator=__snake_case		,     )[0]

      # post-processing
      a		:   List[Any]	  =	self.movq.decode(__snake_case		,     force_not_quantize=__snake_case      )['sample']

      if output_type not in ["pt", "np", "pil"]:
        raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""      )

      if output_type in ["np", "pil"]:
        a		:   int	  =	image * 0.5 + 0.5
        a		:   int	  =	image.clamp(0		,     1      )
        a		:   Union[str, Any]	  =	image.cpu().permute(0		,     2		,     3		,     1      ).float().numpy()

      if output_type == "pil":
        a		:   Optional[Any]	  =	self.numpy_to_pil(__snake_case      )

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__snake_case      )




'''simple docstring'''






import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				a__(   lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       CodeGenTokenizer
    lowercase__					=       CodeGenTokenizerFast
    lowercase__					=       True
    lowercase__					=       {"""add_prefix_space""": True}
    lowercase__					=       False







    def      lowercase_			( self					:    List[str]      ):
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a		:   Any	  =	[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '<unk>',
          '<|endoftext|>',
      ]
      a		:   Dict	  =	dict(zip(__snake_case		,     range(len(__snake_case      )      )      )      )
      a		:   str	  =	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      a		:   List[str]	  =	{'unk_token': '<unk>'}

      a		:   List[str]	  =	os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['vocab_file']      )
      a		:   Dict	  =	os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['merges_file']      )
      with open(self.vocab_file		,     'w'		,     encoding='utf-8'      ) as fp:
        fp.write(json.dumps(__snake_case      ) + '\n'      )
      with open(self.merges_file		,     'w'		,     encoding='utf-8'      ) as fp:
        fp.write('\n'.join(__snake_case      )      )







    def      lowercase_			( self					:    Tuple		,     **__snake_case					:    Dict      ):
      kwargs.update(self.special_tokens_map      )
      return CodeGenTokenizer.from_pretrained(self.tmpdirname		,     **__snake_case      )







    def      lowercase_			( self					:    List[Any]		,     **__snake_case					:    List[str]      ):
      kwargs.update(self.special_tokens_map      )
      return CodeGenTokenizerFast.from_pretrained(self.tmpdirname		,     **__snake_case      )







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    List[Any]      ):
      a		:   Tuple	  =	'lower newer'
      a		:   str	  =	'lower newer'
      return input_text, output_text







    def      lowercase_			( self					:    str      ):
      a		:   List[Any]	  =	CodeGenTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map      )
      a		:   Dict	  =	'lower newer'
      a		:   int	  =	['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
      a		:   List[str]	  =	tokenizer.tokenize(__snake_case		,     add_prefix_space=__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      a		:   Dict	  =	tokens + [tokenizer.unk_token]
      a		:   int	  =	[14, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case      )		,     __snake_case      )







    def      lowercase_			( self					:    List[Any]      ):
      if not self.test_rust_tokenizer:
        return

      a		:   Optional[int]	  =	self.get_tokenizer()
      a		:   Tuple	  =	self.get_rust_tokenizer(add_prefix_space=__snake_case      )

      a		:   List[str]	  =	'lower newer'

      # Testing tokenization
      a		:   Optional[int]	  =	tokenizer.tokenize(__snake_case		,     add_prefix_space=__snake_case      )
      a		:   Optional[Any]	  =	rust_tokenizer.tokenize(__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      # Testing conversion to ids without special tokens
      a		:   Tuple	  =	tokenizer.encode(__snake_case		,     add_special_tokens=__snake_case		,     add_prefix_space=__snake_case      )
      a		:   str	  =	rust_tokenizer.encode(__snake_case		,     add_special_tokens=__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      # Testing conversion to ids with special tokens
      a		:   Optional[int]	  =	self.get_rust_tokenizer(add_prefix_space=__snake_case      )
      a		:   Optional[int]	  =	tokenizer.encode(__snake_case		,     add_prefix_space=__snake_case      )
      a		:   Dict	  =	rust_tokenizer.encode(__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      # Testing the unknown token
      a		:   Any	  =	tokens + [rust_tokenizer.unk_token]
      a		:   List[Any]	  =	[14, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__snake_case      )		,     __snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    int		,     **__snake_case					:    str      ):
      # It's very difficult to mix/test pretokenization with byte-level
      # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
      pass







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    str=15      ):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
          a		:   List[Any]	  =	self.rust_tokenizer_class.from_pretrained(__snake_case		,     **__snake_case      )

          # Simple input
          a		:   Dict	  =	'This is a simple input'
          a		:   int	  =	['This is a simple input 1', 'This is a simple input 2']
          a		:   Dict	  =	('This is a simple input', 'This is a pair')
          a		:   Dict	  =	[
              ('This is a simple input 1', 'This is a simple input 2'),
              ('This is a simple pair 1', 'This is a simple pair 2'),
          ]

          # Simple input tests
          self.assertRaises(__snake_case		,     tokenizer_r.encode		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Simple input
          self.assertRaises(__snake_case		,     tokenizer_r.encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Simple input
          self.assertRaises(
              __snake_case		,     tokenizer_r.batch_encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'		,     )

          # Pair input
          self.assertRaises(__snake_case		,     tokenizer_r.encode		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Pair input
          self.assertRaises(__snake_case		,     tokenizer_r.encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Pair input
          self.assertRaises(
              __snake_case		,     tokenizer_r.batch_encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'		,     )







    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Any	  =	CodeGenTokenizer.from_pretrained(self.tmpdirname		,     pad_token='<pad>'      )

      # Simple input
      a		:   Union[str, Any]	  =	'This is a simple input'
      a		:   Dict	  =	['This is a simple input looooooooong', 'This is a simple input']
      a		:   int	  =	('This is a simple input', 'This is a pair')
      a		:   List[Any]	  =	[
          ('This is a simple input loooooong', 'This is a simple input'),
          ('This is a simple pair loooooong', 'This is a simple pair'),
      ]

      a		:   Optional[int]	  =	tokenizer.pad_token_id

      a		:   List[Any]	  =	tokenizer(__snake_case		,     padding='max_length'		,     max_length=30		,     return_tensors='np'      )
      a		:   Union[str, Any]	  =	tokenizer(__snake_case		,     padding=__snake_case		,     truncate=__snake_case		,     return_tensors='np'      )
      a		:   Optional[Any]	  =	tokenizer(*__snake_case		,     padding='max_length'		,     max_length=60		,     return_tensors='np'      )
      a		:   List[Any]	  =	tokenizer(__snake_case		,     padding=__snake_case		,     truncate=__snake_case		,     return_tensors='np'      )

      # s
      # test single string max_length padding
      self.assertEqual(out_s['input_ids'].shape[-1]		,     30      )
      self.assertTrue(pad_token_id in out_s['input_ids']      )
      self.assertTrue(0 in out_s['attention_mask']      )

      # s2
      # test automatic padding
      self.assertEqual(out_sa['input_ids'].shape[-1]		,     33      )
      # long slice doesn't have padding
      self.assertFalse(pad_token_id in out_sa['input_ids'][0]      )
      self.assertFalse(0 in out_sa['attention_mask'][0]      )
      # short slice does have padding
      self.assertTrue(pad_token_id in out_sa['input_ids'][1]      )
      self.assertTrue(0 in out_sa['attention_mask'][1]      )

      # p
      # test single pair max_length padding
      self.assertEqual(out_p['input_ids'].shape[-1]		,     60      )
      self.assertTrue(pad_token_id in out_p['input_ids']      )
      self.assertTrue(0 in out_p['attention_mask']      )

      # p2
      # test automatic padding pair
      self.assertEqual(out_pa['input_ids'].shape[-1]		,     52      )
      # long slice pair doesn't have padding
      self.assertFalse(pad_token_id in out_pa['input_ids'][0]      )
      self.assertFalse(0 in out_pa['attention_mask'][0]      )
      # short slice pair does have padding
      self.assertTrue(pad_token_id in out_pa['input_ids'][1]      )
      self.assertTrue(0 in out_pa['attention_mask'][1]      )







    def      lowercase_			( self					:    Optional[int]      ):
      a		:   int	  =	'$$$'
      a		:   Dict	  =	CodeGenTokenizer.from_pretrained(self.tmpdirname		,     bos_token=__snake_case		,     add_bos_token=__snake_case      )

      a		:   int	  =	'This is a simple input'
      a		:   Tuple	  =	['This is a simple input 1', 'This is a simple input 2']

      a		:   Any	  =	tokenizer.bos_token_id

      a		:   Union[str, Any]	  =	tokenizer(__snake_case      )
      a		:   Dict	  =	tokenizer(__snake_case      )

      self.assertEqual(out_s.input_ids[0]		,     __snake_case      )
      self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids      )      )

      a		:   Optional[Any]	  =	tokenizer.decode(out_s.input_ids      )
      a		:   Optional[Any]	  =	tokenizer.batch_decode(out_sa.input_ids      )

      self.assertEqual(decode_s.split()[0]		,     __snake_case      )
      self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa      )      )







    @slow
    def      lowercase_			( self					:    List[Any]      ):
      a		:   Tuple	  =	CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono'      )

      a		:   Union[str, Any]	  =	'\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
      a		:   Optional[Any]	  =	'\nif len_a > len_b:      result = a\nelse:      result = b'

      a		:   Any	  =	tokenizer.encode(__snake_case      )
      a		:   Union[str, Any]	  =	['^#', re.escape('<|endoftext|>'      ), '^\'\'\'', '^"""', '\n\n\n']
      a		:   List[str]	  =	tokenizer.decode(__snake_case		,     truncate_before_pattern=__snake_case      )
      self.assertEqual(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Any      ):
      pass